DLTK documentation¶
DLTK is a neural networks toolkit written in python, on top of Tensorflow. Its modular architecture is closely inspired by sonnet and it was developed to enable fast prototyping and ensure reproducibility in image analysis applications, with a particular focus on medical imaging. Its goal is to provide the community with state of the art methods and models and to accelerate research in this exciting field.
Road map¶
Over the course of the next months we will add more content to DLTK. This road map outlines the immediate plans for what you will be seeing in DLTK soon:
- Medical model zoo
- Pre-trained models on medical images and deploy scripts
- Core
- Losses: Dice loss, frequency reweighted losses, adversial training Normalisation: layer norm, weight norm
- Network architectures
- deepmedic, densenet, VGG, super-resolution networks
- Other
- Augmentation via elastic deformations Sampling with fixed class frequencies
Installation¶
DLTK uses the following dependencies:
- numpy
- scipy
- Tensorflow: Installation Instructions
Use pip to install DLTK:
pip install dltk
Core Team¶
Martin Rajchl [github, twitter]
Nick Pawlowski [github, twitter]
BioMedIA group, Dept. of Computing, Imperial College London